Partner with Product and BI to turn business goals into well-scoped ML problems with clear success criteria and guardrails
Build and productionize models for segmentation, churn, LTV, and recommendations
Design evaluation frameworks including offline benchmarks and online feedback loops to ensure models are measured, not assumed
Collaborate with Engineering on production design considerations like latency, payload structure, fallbacks, and failure modes
Monitor model performance over time, detect drift, and own retraining and iteration cycles
Use product telemetry and user feedback to drive continuous model and feature improvements
Work alongside AI coding agents to accelerate development (pipelines, tests, refactoring, exploration) while maintaining human oversight
Communicate results, trade-offs, and recommendations clearly across technical and non-technical teams

Flexibility: People-first approach focused on outcomes, not location or hours.
Collaboration: Partner closely with Product, Engineering, and BI in a fast-moving, supportive environment.

Data Scientist

Key skills